This study investigated whether IVF lab workload can be optimized without compromising clinical outcomes. Using simulations based on 774 real IVF cycles, various scheduling strategies were tested by adjusting trigger days. The balanced optimization approach reduced weekend workload by 20% while maintaining a high yield of mature (M2) oocytes. Unlike pure workload or M2-focused strategies, this method effectively balanced staff efficiency and clinical success. The model demonstrated that AI-driven scheduling can improve daily workload distribution without affecting outcome quality. These findings suggest that IVF labs can adopt such strategies to enhance efficiency and staff well-being. Further real-world validation would strengthen clinical applicability.